CO-OCCURRENCE NETWORK OF REUTERS NEWS
Arzucan Özgür (),
Burak Cetin () and
Haluk Bingol ()
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Arzucan Özgür: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
Burak Cetin: Complex Systems Research Laboratory, Department of Computer Engineering, Bogazici University, Istanbul, Turkey
Haluk Bingol: Complex Systems Research Laboratory, Department of Computer Engineering, Bogazici University, Istanbul, Turkey
International Journal of Modern Physics C (IJMPC), 2008, vol. 19, issue 05, 689-702
Networks describe various complex natural systems including social systems. We investigate the social network of co-occurrence in Reuters-21578 corpus, which consists of news articles that appeared in the Reuters newswire in 1987. People are represented as vertices and two persons are connected if they co-occur in the same article. The network has small-world features with power-law degree distribution. The network is disconnected and the component size distribution has power-law characteristics. Community detection on a degree-reduced network provides meaningful communities. An edge-reduced network, which contains only the strong ties has a star topology. "Importance" of persons are investigated. The network is the situation in 1987. After 20 years, a better judgment on the importance of the people can be done. A number of ranking algorithms, including Citation count and PageRank, are used to assign ranks to vertices. The ranks given by the algorithms are compared against how well a person is represented in Wikipedia. We find up to medium level Spearman's rank correlations. A noteworthy finding is that PageRank consistently performed worse than the other algorithms. We analyze this further and find reasons.
Keywords: Social networks; importance of people; ranking; Wikipedia; 89.75.Fb; 89.75.-k; 89.65.-s (search for similar items in EconPapers)
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